Deep Neural Networks for Estimation of Gamma-Ray Burst Redshifts
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arXiv
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| Natura: | Preprint |
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2024
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| author | Aldowma, Tamador Razzaque, Soebur |
| author_facet | Aldowma, Tamador Razzaque, Soebur |
| contents | While the available set of Gamma-ray Burst (GRB) data with known redshift is currently limited, a much larger set of GRB data without redshift is available from different instruments. This data includes well-measured prompt gamma-ray flux and spectral information. We estimate the redshift of a selection of these GRBs detected by Fermi-GBM and Konus-Wind using Machine Learning techniques that are based on spectral parameters. We find that Deep Neural Networks with Random Forest models employing non-linear relations among input parameters can reasonably reproduce the pseudo-redshift distribution of GRBs, mimicking the distribution of GRBs with spectroscopic redshift. Furthermore, we find that the pseudo-redshift samples of GRBs satisfy (i) Amati relation between the peak photon energy of the time-averaged energy spectrum in the cosmological rest frame of the GRB ${E}_{\rm i, p}$ and the isotropic-equivalent radiated energy ${E}_{\rm iso}$ during the prompt phase; and (ii) Yonetoku relation between ${E}_{\rm i, p}$ and isotropic-equivalent luminosity ${L}_{\rm iso}$, both measured during the peak flux interval. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_11005 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Neural Networks for Estimation of Gamma-Ray Burst Redshifts Aldowma, Tamador Razzaque, Soebur High Energy Astrophysical Phenomena Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics While the available set of Gamma-ray Burst (GRB) data with known redshift is currently limited, a much larger set of GRB data without redshift is available from different instruments. This data includes well-measured prompt gamma-ray flux and spectral information. We estimate the redshift of a selection of these GRBs detected by Fermi-GBM and Konus-Wind using Machine Learning techniques that are based on spectral parameters. We find that Deep Neural Networks with Random Forest models employing non-linear relations among input parameters can reasonably reproduce the pseudo-redshift distribution of GRBs, mimicking the distribution of GRBs with spectroscopic redshift. Furthermore, we find that the pseudo-redshift samples of GRBs satisfy (i) Amati relation between the peak photon energy of the time-averaged energy spectrum in the cosmological rest frame of the GRB ${E}_{\rm i, p}$ and the isotropic-equivalent radiated energy ${E}_{\rm iso}$ during the prompt phase; and (ii) Yonetoku relation between ${E}_{\rm i, p}$ and isotropic-equivalent luminosity ${L}_{\rm iso}$, both measured during the peak flux interval. |
| title | Deep Neural Networks for Estimation of Gamma-Ray Burst Redshifts |
| topic | High Energy Astrophysical Phenomena Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2401.11005 |